Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience, make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents powered by large language models (LLMs) have demonstrated notable applications across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical examinations. However, current agent frameworks are not well-adapted to real-world clinical scenarios, especially those involving the complex demands of rare diseases. To bridge this gap, we introduce RareAgents, the first LLM-driven multi-disciplinary team decision-support tool designed specifically for the complex clinical context of rare diseases. RareAgents integrates advanced Multidisciplinary Team (MDT) coordination, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents outperforms state-of-the-art domain-specific models, GPT-4o, and current agent frameworks in diagnosis and treatment for rare diseases. Furthermore, we contribute a novel rare disease dataset, MIMIC-IV-Ext-Rare, to facilitate further research in this field.

Lingxiao Luo, Bingda Tang, Xuanzhong Chen, Rong Han, Ting Chen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.

Text input is essential in tablet computer interaction. However, tablet software keyboards face the problem of misrecognizing unintentional touch, which affects efficiency and usability [29, 49]. In this paper, we proposed TypeBoard, a pressure-sensitive touchscreen keyboard that prevents unintentional touches. The TypeBoard allows users to rest their fingers on the touchscreen, which changes the user behavior: on average, users generate 40.83 unintentional touches every 100 keystrokes. The TypeBoard prevents unintentional touch with an accuracy of 98.88%. A typing study showed that the TypeBoard reduced fatigue (p < 0.005) and typing errors (p < 0.01), and improved the touchscreen keyboard’ typing speed by 11.78% (p < 0.005). As users could touch the screen without triggering responses, we added tactile landmarks on the TypeBoard, allowing users to locate the keys by the sense of touch. This feature further improves the typing speed, outperforming the ordinary tablet keyboard by 21.19% (p < 0.001). Results show that pressure-sensitive touchscreen keyboards can prevent unintentional touch, improving usability from many aspects, such as avoiding fatigue, reducing errors, and mediating touch typing on tablets.